Cmin - herramienta case basada en crisp-dm para el soporte de proyectos de minería de datos

En este artículo se presenta la CMIN, una herramienta CASE (Computer Aided Software Engineering) integrada (que soporta todas las fases de un proceso) basada en CRISP-DM 1.0 (Cross – Industry Standard Process for Data Mining) para soportar el desarrollo de proyectos de minería de datos.Primero se expone la funcionalidad general de CMIN, lo que incluye la gestión de procesos, plantillas y proyectos, y se destaca la capacidad de CMIN para realizar el seguimiento de los proyectos de una forma fácil e intuitiva y la manera como CMIN posibilita que el usuario incremente su conocimiento en el uso de CRISP-DM o de cualquier otro proceso que se defina en la herramienta a través de las ayudas e información que se ofrece en cada paso del proceso. Después, se detalla cómo CMIN permite enlazar en tiempo de ejecución (sin necesidad de volver a compilar la herramienta) nuevos algoritmos de minería de datos que apoyen la labor de modelado (basada en un flujo de trabajo o workflow) en un proyecto de minería de datos. Finalmente, se ofrecen los resultados de dos evaluaciones de la herramienta, las conclusiones y el trabajo futuro.This paper introduces CMIN, an integrated computer aided software engineering (CASE) tool based on cross-industry standard process for data mining (CRISP-DM) 1.0 designed to support carrying out data mining projects. It is “integrated” in the sense that it supports all phases of a process. A general overview of how CMIN works is presented first, including a treatment of processes, templates and project management. CMIN’s capacity for easily and intuitively monitoring projects is highlighted, as is the manner in which CMIN allows a user to increase knowledge regarding using CRISP-DM or any other process defined in the CASE tool through the help and information presented in each step. Next, it is shown how CMIN can bind new data mining algorithms in runtime (without the need to recompile the tool) to support modelling tasks (based on a Workflow) and evaluate data mining projects. Finally, the results of two evaluations of the tool, some conclusions and suggestions for future work are presented

A comparison of alternative technology adoption models : the adoption of a CASE tool at a university

Bibliography: leaves 95-105.In a field such as that of Information Systems the emergence of new technologies is one of the only constants. It is therefore necessary, indeed vital, to be able to measure, as well as anticipate, the adoption and diffusion of these new technologies into organisations. For this purpose adoption models came to the fore. Such models include the Technology Acceptance Model (TAM) (Davis, 1989), the Technology Acceptance Model 2 (T AM2) (Venkatesh & Davis, 2000), the Decomposed Theory of Planned Behaviour (DTPB) (Taylor & Todd, 1995b), and the Perceived Characteristics of Innovating model (PCI) (Moore & Benbasat, 1991). Adoption models test the perceptions and attitudes of potential and actual adopters of a new technology. Although all of the adoption models test adoption of a new technology, each tests different aspects of this adoption. Through the comparison of the four adoption models mentioned above, this study determines which constructs mostly strongly explain the adoption of a CASE tool by university students. These constructs are then combined to form a new technology adoption model, the Perceived Characteristics of Technology Adoption CPCTA), which is tested and found to explain a significant degree of variance in the context of CASE tool adoption amongst students at a university